Prospective memory in the red zone: Cognitive control and capacity sharing in a complex, multi-stimulus task.

Strickland, Luke; Elliott, David; Wilson, Micah K.; Loft, Shayne; Neal, Andrew; Heathcote, Andrew · 2019 · OpenAlex-citations

DOI: 10.1037/xap0000224

archive: archived pipeline: cataloged verified

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Summary

This study investigates event-based prospective memory (PM) within high-demand environments, specifically addressing how cognitive resources are allocated when task demands approach the "red zone"—a level of workload where capacity is no longer in surplus. While previous research using simple laboratory paradigms suggested that PM monitoring relies on surplus resources without impairing ongoing task performance, this paper tests whether capacity sharing occurs in complex, dynamic settings. The authors apply the Prospective Memory Decision Control (PMDC) model to a simulated maritime surveillance task to determine if PM performance in such environments involves trade-offs between monitoring for PM cues and executing the primary surveillance task. The experimental design utilized a validated maritime surveillance simulation where participants monitored three ships moving across a display, classifying them as targets or non-targets based on a complex seven-feature rule. The task included visual noise and time pressure, with a nine-second deadline per trial, placing participants near the previously identified performance "redline." Participants performed both control blocks (ongoing task only) and PM blocks, where they had to make an alternative response to ships displaying specific features (a flag and a lifeboat). The study involved 36 undergraduate participants. Data were analyzed using the PMDC model, an evidence accumulation framework that estimates latent cognitive variables such as accumulation rates (processing speed/quality) and decision thresholds (strategic control). This allowed the researchers to distinguish between proactive control (anticipatory threshold adjustments) and reactive control (stimulus-driven accumulation changes), as well as to detect capacity sharing by comparing processing rates in PM versus control conditions. The results provided evidence of capacity sharing between PM monitoring and the ongoing surveillance task, a finding that contrasts with previous studies using simpler paradigms. Specifically, the demanding nature of the ongoing task limited available cognitive resources, causing PM demands to incur a cost to the accumulation rates of non-PM items. Additionally, participants employed both proactive and reactive control mechanisms to adapt to the high workload. Proactive control was observed through adjustments to decision thresholds, while reactive control manifested as inhibitory effects on ongoing task accumulation when processing PM targets. These findings demonstrate that in complex environments, PM is not supported by reserve capacity but rather competes for limited resources with primary task processes. The significance of this work lies in bridging the gap between basic cognitive research and practical human factors applications. By validating the PMDC model in a complex, multi-stimulus environment, the study illustrates that quantitative modeling can reveal latent cognitive mechanisms—such as capacity sharing and adaptive control strategies—that are obscured in simpler tasks. These insights have direct implications for work design in industrial systems like air traffic control and maritime surveillance, highlighting the need to account for resource competition when operators face high cognitive loads. The study underscores the value of using human factors simulations to identify performance limits and optimize system design to prevent errors in the "red zone."

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• prospective memory interruptions